Condition based monitoring of irrigation

ABSTRACT

A monitoring system for an irrigation system is presented. The irrigation system includes a movable end gun operatively associated with a portion of the irrigation system. The monitoring system includes a sensor configured to generate an electrical signal indicative of movement and/or positioning of the movable end gun relative to the portion of the irrigation system over time, a processor, and a memory. The memory includes instructions stored thereon, which when executed by the processor cause the system to: receive the generated electrical signal, determine whether the movable end gun, or one or more components thereof, requires maintenance based on the electrical signal, and determine when the moveable end gun is in an on and/or off trigger state based on the electrical signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication No. 63/129,799, filed on Dec. 23, 2020, the entire contentsof which are hereby incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to irrigation systems and, more particularly, tostructures and methods for effectuating predictive maintenance ofirrigation systems.

BACKGROUND

Irrigation systems such as pivots, lateral move systems, drip irrigationsystems, etc. breakdown on average three times per year out of 40 uses.These breakdowns occur during critical growing steps and in many casesin the middle of the field.

SUMMARY

To limit delays, increased costs and other problems associated withirrigation system breakdown, this disclosure details a solutionincluding digital observation of the irrigation system during normaloperation and set parameters that indicate abnormal operation. Toobserve these operational anomalies, sensors may be added to theirrigation system to provide data for algorithms to process. Thesealgorithms may be logic or analytics based. Existing operational datafrom off the shelf may be used in some cases. In aspects, other datasources may be external to the system, such as National Oceanic andAtmospheric Administration (NOAA) weather, topographical maps, soilmoisture, etc., or combinations thereof.

According to one aspect, a monitoring system for an irrigation system ispresented. The irrigation system includes a movable end gun operativelyassociated with a portion of the irrigation system. The monitoringsystem includes a sensor configured to couple to the movable end gun andconfigured to generate an electrical signal indicative of movementand/or positioning of the movable end gun relative to the portion of theirrigation system over time, a processor, and a memory. The memoryincludes instructions stored thereon, which when executed by theprocessor, cause the system to: receive the generated electrical signal,determine whether the movable end gun, or one or more componentsthereof, requires maintenance based on the electrical signal, anddetermine when the moveable end gun is in an on and/or off trigger statebased on the electrical signal.

In another aspect of the present disclosure, the instructions, whenexecuted, may further cause the system to determine an angular rate ofthe moveable end gun and time spent going forward and/or reverse basedon the electrical signal.

In yet another aspect of the present disclosure, the instructions, whenexecuted, may further cause the system to generate a report based on thedeterminations.

In a further aspect of the present disclosure, the instructions, whenexecuted, may further cause the monitoring system to determine if theend gun pivots more than a predetermined number of degrees without anend gun on trigger state and provide an indication to a user that alocation was not irrigated based on the determination.

In yet a further aspect of the present disclosure, wherein the portionof the irrigation system includes at least one of a lateral drive, awater winch, or a pivot, and the movable end gun may be movably mountedon the pivot.

In another aspect of the present disclosure, the movable end gun may bepart of the same system but separate from the portion of the irrigationsystem.

In yet another aspect of the present disclosure, the system may furtherinclude an analytics engine configured to perform the determinations.

In a further aspect of the present disclosure, the instructions, whenexecuted by the processor, may further cause the monitoring system toreceive data from at least one of a weather station, a field soilmoisture sensor, a terrain and soil map, a temperature sensor, orNational Oceanic and Atmospheric Administration weather.

In yet a further aspect of the present disclosure, the analytics enginemay include a machine learning model, and wherein the machine learningmodel is based on a deep learning network, a classical machine learningmodel, or combinations thereof.

In another aspect of the present disclosure, the sensor may include anencoder, a pressure sensor, a flow meter, a magnetometer, a gyroscope,an accelerometer, a camera, a gesture sensor, a microphone, a laserrange finder, a reed switch, a magnetic switch, a GPS, and/or an opticalswitch.

In an aspect of the present disclosure, a computer-implemented methodfor monitoring an irrigation system including four end gun zones ispresented. Each end gun zone includes a movable end gun operativelyassociated with a portion of the irrigation system. The method includesreceiving an electrical signal generated by a sensor configured tocouple to the movable end gun, wherein the electrical signal isindicative of movement and/or positioning of the movable end gunrelative to the portion of the irrigation system over time, determiningwhether the movable end gun, or one or more components thereof, requiresmaintenance based on the electrical signal, and determining when themoveable end gun is in an on and/or off trigger state based on theelectrical signal.

In yet another aspect of the present disclosure, the method may furtherinclude determining an angular rate of the moveable end gun and timespent going forward and/or reverse based on the electrical signal.

In a further aspect of the present disclosure, the method may furtherinclude generating a report based on the determinations.

In yet a further aspect of the present disclosure, the method mayfurther include determining if the end gun pivots more than apredetermined number of degrees without an end gun on trigger state andproviding an indication to a user that a location was not irrigatedbased on the determination.

In another aspect of the present disclosure, wherein the portion of theirrigation system includes at least one of a lateral drive, a waterwinch, or a pivot, and the movable end gun may be movably mounted on thepivot.

In yet another aspect of the present disclosure, the movable end gun maybe part of the same system but separate from the portion of theirrigation system.

In a further aspect of the present disclosure, the method may furtherinclude performing the determinations by an analytics engine.

In yet a further aspect of the present disclosure, the method mayfurther include receiving data from at least one of a weather station, afield soil moisture sensor, a terrain and soil map, a temperaturesensor, or National Oceanic and Atmospheric Administration weather.

In yet another aspect of the present disclosure, the analytics engineincludes a machine learning model, and wherein the machine learningmodel is based on a deep learning network, a classical machine learningmodel, or combinations thereof.

In an aspect of the present disclosure, a non-transitorycomputer-readable medium stores instructions that, when executed by aprocessor, cause the processor to perform a method for monitoring anirrigation system is presented. The irrigation system includes aplurality of end gun zones. Each end gun zone, of the plurality of endgun zones, includes a movable end gun operatively associated with aportion of the irrigation system. The method includes receiving anelectrical signal generated by a sensor configured to couple to themovable end gun, wherein the electrical signal is indicative of movementand/or positioning of the movable end gun relative to the portion of theirrigation system over time, determining whether the movable end gun, orone or more components thereof, requires maintenance based on theelectrical signal, and determining when the moveable end gun is in an onand/or off trigger state based on the electrical signal.

Other aspects, features, and advantages will be apparent from thedescription, the drawings, and the claims that follow.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate aspects of the disclosure and,together with a general description of the disclosure given above andthe detailed description given below, serve to explain the principles ofthis disclosure, wherein:

FIG. 1 is a diagram illustrating a monitoring system;

FIG. 2 is a block diagram of a controller configured for use with thepredictive maintenance system of FIG. 1;

FIG. 3 is a diagram illustrating a machine learning model configured foruse with the predictive maintenance system of FIG. 1;

FIG. 4A illustrates an exemplary flow chart of a typical farm operation;

FIG. 4B illustrates an exemplary flow chart of a farm operationincluding a predictive maintenance system in accordance with theprinciples of this disclosure;

FIG. 5 illustrates a data science work-flow with various models of thepredictive maintenance system illustrated in FIG. 1;

FIGS. 6-8 are diagrams of example hardware interface and instrumentationof the predictive maintenance system of FIG. 1;

FIG. 9 is perspective view of a portion of an exemplary pivot of thepredictive maintenance system of FIG. 1;

FIG. 10 is a perspective view of a portion of air compressorinstrumentation of another exemplary pivot of the predictive maintenancesystem of FIG. 1;

FIG. 11A is a perspective view of an end gun assembly of the predictivemaintenance system in accordance with principles of this disclosure;

FIG. 11B is a side view of the end gun assembly of FIG. 11A;

FIGS. 12A-12C are example signals generated with the predictivemaintenance system of FIG. 11A;

FIGS. 13A and 13B illustrate exemplary flow charts of predictivemaintenance systems including end gun monitoring in accordance withprinciples of this disclosure;

FIG. 14 illustrates exemplary data science work-flow of the predictivemaintenance systems of this disclosure;

FIGS. 15-17 are illustrative flow charts for testing systems of thepredictive maintenance systems of this disclosure;

FIG. 18 is an illustrative model for end gun performance predictionusing a nine degree of freedom inertial measurement unit;

FIGS. 19-21 are high level block diagrams for a monitoring system inaccordance with the disclosure;

FIG. 22 is a state machine diagram for the system of FIG. 27 inaccordance with the disclosure;

FIG. 23 is a diagram of end gun quadrants in a field being utilized fordetermining irrigation failure locations, in accordance with thedisclosure;

FIG. 24 is a graph of example gyro signal output for the monitoringsystem of FIG. 1, in accordance with the disclosure;

FIG. 25 is a graph of example compass signal output for the monitoringsystem of FIG. 1, in accordance with the disclosure; and

FIG. 26 is a graph of example accelerometer signal output for themonitoring system of FIG. 1, in accordance with the disclosure.

DETAILED DESCRIPTION

Aspects of the disclosed predictive maintenance systems are described indetail with reference to the drawings, in which like reference numeralsdesignate identical or corresponding elements in each of the severalviews. Directional terms such as top, bottom, and the like are usedsimply for convenience of description and are not intended to limit thedisclosure attached hereto. Also, as used herein, the term “on” includesbeing in an open or activated position, whereas the term “off” includesbeing in a closed or inactivated position.

In the following description, well-known functions or constructions arenot described in detail to avoid obscuring the present disclosure inunnecessary detail.

Advantageously, the disclosed system predicts common unexpected downtimeversus notification that it occurred after the fact. The disclosedsystem provides better insight than a team driving around to observeoperation (which can be subjective). Technology today only notifies offailure after the failure has occurred, whereas the disclosed systempredicts a maintenance requirement before the failure occurs.

With reference to FIGS. 1 and 7-9, a monitoring system 100 for anirrigation system (for farming, mining, etc.) is provided. Generally,the monitoring system 100 includes an irrigation system 106 and acontroller 200 configured to execute instructions controlling theoperation of the pivot monitoring system 100. The irrigation system 106may include a pump 10 (e.g., a compressor or booster pump, see FIG. 11),a pivot 20, one or more towers 30, an end tower 40, a corner tower 50,an air compressor 60, and an end gun 70 (also known as a big gun, bigvolume gun, and/or movable nozzle). The pump 10 may include one or morecurrent sensors and a wireless communication device 104 configured totransmit data wirelessly to the controller 200 (e.g., sensed currentdata). The pivot 20 may include one or more sensors 102 and a wirelesscommunication device 104 configured to transmit data wirelessly to thecontroller 200. Each tower 30, corner tower 50, and end tower 40 mayinclude one or more sensors 102 and a wireless communication device 104configured to transmit data wirelessly to the controller 200. Thewireless communication device may include, for example, 3G, LTE, 4G, 5G,Bluetooth, and/or Wi-Fi, etc. The sensors 102 may include at least oneof a current sensor, a voltage sensor, and/or a power sensor configuredto sense, for example, current, voltage, and/or power, respectively. Inaspects, these sensors 102 may measure the transmission of electricityinto a motor of the booster pump 10 motor when part of the system. Thepump 10 may include the transmission lines on the span; a contactor; andcomponents used to actuate the contactor, the motor components includingthe electrical components, mechanical components, and the pumpcomponents including the impeller, inlet, outlet, and/or tubing. Inaspects, the pump 10 may include a flow sensor (not shown) on thebooster pump outlet.

In aspects, the one or more sensors 102 can include any suitable sensorssuch as, for example, an encoder (e.g., an angular encoder), pressuresensor, flow meter, etc., or combinations thereof. An angular encodermay be in a form of position sensor that measures the angular positionof a rotating shaft.

In aspects, the one or more sensors may be connected (e.g., directly)and/or may be standalone components that may be connected via wide areanetwork (WAN). In aspects, the one or more sensors may be aggregated inthe cloud based on provisioning settings. In aspects, the one or moresensors may include, for example, low-power wide area network technology(LPWAN) which may be long-range (LoRa).

In aspects, the controller 200 may determine changes in the condition ofthe at least one component based on comparing the generated signal topredetermined data.

The controller 200 is configured to receive data from the sensors 102 aswell as from external data sources such as weather stations 82, fieldsoil moisture sensors 86, terrain and soil maps 88, temperature sensors89, and/or National Oceanic and Atmospheric Administration (NOAA)weather 84 to make and/or refine predictions indicative of a conditionof at least one component (e.g., a pivot 20, an end gun 70, a tower 30,etc.) of the plurality of components of the irrigation system 106. Thisprediction enables the controller 200 to determine changes in thecondition of the at least one component and predict maintenancerequirements of the at least one component based on predetermined data(e.g., historical data). For example, the prediction may be based oncomparing the determined changes in the condition of at least onecomponent of the irrigation system 106 to predetermined data. Forexample, the sensor 102 of a tower 30 may sense the typical current drawof that tower 30. The sensed current draw may then be compared by thecontroller 200 to historical and/or typical tower current draw. Thecontroller may determine that the sensed current draw of this tower 30is considerably higher than the historical current draw by apredetermined number (e.g., about 30%) for a particular set ofconditions (sunny day, dry soil, etc.). Based on this determination, thecontroller 200 may predict that this tower 30 needs maintenance.Additionally, the specific type of maintenance may be able to bepredicted. For example, if the motor current of a tower 30 is high, itmay indicate a flat tire. The pivot monitoring system 100 mayadditionally or alternatively predict the number of hours typicallytaken to repair such an occurrence. In another example, the system maysense, by the sensor 102 that the current on a pump 10 is low, andaccordingly, predict that there is a pump 10 failure. In an example, aterrain map identifies if the pivot 20 is sloped down-hill, thusincreasing the pressure at the end gun 70, which facilitates adetermination of why pressure is higher for that particular area.

Data from the external data sources may be used to improve modelpredictions. For example, by processing data such as higher power use tomotors of the towers 30 because the field is muddy due to recent rain,such processed data can be used to improve model predictions. The pivotmonitoring system 100 may display field maps for terrain, soil types,etc., that help the model explain variation in power use. Thepredictions may be transmitted to a user device 120, by the controller200, for display and/or further analysis.

In aspects, the data and/or predictions may be processed by a datavisualization system 110. Data visualization is the graphicalrepresentation of information and data. By using visual elements likecharts, graphs, and maps, data visualization tools provide an accessibleway to see and understand trends, outliers, and patterns in data.

In aspects, the pivot monitoring system 100 may be implemented in thecloud. For instance, Linux, which may run a Python script, for example,can be utilized to effectuate prediction.

FIG. 2 illustrates that controller 200 includes a processor 220connected to a computer-readable storage medium or a memory 230. Thecomputer-readable storage medium or memory 230 may be a volatile type ofmemory, e.g., RAM, or a non-volatile type of memory, e.g., flash media,disk media, etc. In various aspects of the disclosure, the processor 220may be another type of processor, such as a digital signal processor, amicroprocessor, an ASIC, a graphics processing unit (GPU), afield-programmable gate array (FPGA), or a central processing unit(CPU). In certain aspects of the disclosure, network inference may alsobe accomplished in systems that have weights implemented as memristors,chemically, or other inference calculations, as opposed to processors.

In aspects of the disclosure, the memory 230 can be random accessmemory, read-only memory, magnetic disk memory, solid-state memory,optical disc memory, and/or another type of memory. In some aspects ofthe disclosure, the memory 230 can be separate from the controller 200and can communicate with the processor 220 through communication busesof a circuit board and/or through communication cables such as serialATA cables or other types of cables. The memory 230 includescomputer-readable instructions that are executable by the processor 220to operate the controller 200. In other aspects of the disclosure, thecontroller 200 may include a network interface 240 to communicate withother computers or to a server. A storage device 210 may be used forstoring data.

The disclosed method may run on the controller 200 or on a user device,including, for example, on a mobile device, an IoT device, or a serversystem.

In aspects, an analytics engine (e.g., a machine learning model, and/orclassical analytics) may be configured to perform the determinations.

FIG. 3 illustrates a machine learning model 300 anddataflow\storage\feedback of the pivot predictive maintenance system.The machine learning model 300 can be hosted at the pivot 20 or in thecloud (e.g., a remote server). The machine learning model 300 mayinclude one or more convolutional neural networks (CNN).

In machine learning, a convolutional neural network (CNN) is a class ofartificial neural network (ANN), most commonly applied to analyzingvisual imagery. The convolutional aspect of a CNN relates to applyingmatrix processing operations to localized portions of an image, and theresults of those operations (which can involve dozens of differentparallel and serial calculations) are sets of many features that areused to train neural networks. A CNN typically includes convolutionlayers, activation function layers, and pooling (typically max pooling)layers to reduce dimensionality without losing too many features.Additional information may be included in the operations that generatethese features. Providing unique information that yields features thatgive the neural networks information can be used to ultimately providean aggregate way to differentiate between different data input to theneural networks. In aspects, the machine learning model 300 may includea combination of one or more deep learning networks (e.g., a CNN), andclassical machine learning models (e.g., an SVM, a decision tree, etc.).For example, the machine learning model 300 may include two deeplearning networks.

In aspects, two labeling methods for the training data may be used, onebased on a connection with a computer maintenance system (CMMS) and onebased on user input. In aspects, the user can be prompted to label data,or can provide the data manually (e.g., at time of service events).

As noted above, FIG. 4A illustrates an exemplary flow chart of a typicalfarm operation 400 a. Generally, at step 410, pre-season maintenance isperformed on the irrigation equipment. Next, at step 420, the irrigationequipment is used in season. At step 440, if equipment is determined tohave broken down, it is sent in for repair at step 430. At the end ofthe season (step 450), post-season maintenance is performed (step 460).

FIG. 4B illustrates an exemplary flow chart 400 b of a farm operationincluding a monitoring system 100 in accordance with the principles ofthis disclosure. Generally, at step 410, pre-season maintenance isperformed on the irrigation equipment. Next, the monitoring system 100predicts whether maintenance is needed for a particular piece of theirrigation equipment. If maintenance is predicted at step 415, then atstep 430, the equipment is examined and repaired. Next, at step 420, theirrigation equipment is used in season. At step 440, if equipment isdetermined to have broken down, the equipment is sent in for repair atstep 430. At the end of the season (step 450), post-season maintenanceis performed (step 460).

FIG. 5 illustrates a data science work-flow with various models of thepredictive maintenance system illustrated in FIG. 4B.

The five models include an end gun prediction model 502, a tower driveprediction model 504, a sequencing prediction model 506, an aircompression prediction model 508, and an electrical prediction model510. The models may be implemented via logic and/or machine learning.

With reference to FIGS. 5 and 18, an end gun prediction model 502 isshown. The end gun prediction model may count the number of times theend gun 70 (FIG. 1) takes to pass from left to right and back. Expectedtime to pass left and right may be based on pressure, bearing condition,tension, etc., or combinations thereof.

The end gun prediction model 502 can consider expected power based onsoil moisture directly measured or inferred from weather data from thefield or regional weather stations, topographical maps, soil maps, motorRPM, gearbox ratio, tower weight, span weight, operating condition,etc., or combinations thereof. The end gun 70 includes instrumentationwhich can measure each cycle using a proximity switch, encoder,capacitance, and/or image system. Aspects of the monitoring system 100can be mounted on or off the irrigation system 106, for example, amoisture sensor that logs when the moisture sensor is splashed remotelyby the water being distributed to the field. If an electronic gun isused, energy use and duty cycle can be used. In aspects, the one or moresensors can include any suitable sensors such as, for example an encoder(e.g., angular), pressure sensor, flow meter, magnetometer, gyroscope,accelerometer, camera, gesture sensor, microphone, laser range finder,reed/magnetic/optical switch, etc., or combinations thereof. The end gunprediction model 502 may also include as inputs the pump pressure, themodel number of the end gun, the end gun nozzle diameter, the drive armspring setting, the diffuser type, a flow measurement, a drive armspring K-factor, a drive arm balance, a drive arm bearing condition, abase bearing condition, a base seal condition, a drive arm alignment,and/or a mounting base rigidity (FIG. 18). The nozzle type can beinferred from a measured flow and measured pressure. In aspects, the endgun prediction model 502 (FIG. 18) may predict a drive arm impactfrequency, an acceleration magnitude per drive arm impact, an angularrate forward, an angular rate reverse, a heading change rate forward orreverse, a time per pass, and/or a time to flip a reversing lever. Themodel outputs can be used to further predict abnormal operation.

Abnormal operation of the end gun may be further based on movementand/or positioning of the movable end gun 70 relative to the pivot 20(and/or other portion of the irrigation system, such as a lateral drive,a water winch, etc.) over time. For example, the pivot monitoring system100 may monitor the drive arm frequency using an accelerometer and/orgyroscope, and/or the heading change of the end gun 70 may be determinedby a magnetometer. The end gun 70 may typically be “on” for aboutfifteen degrees of rotation from the time it is started to the time itis stopped. The sensor 102 may sense that the end gun 70 was on forabout three degrees of rotation and the controller may determine thatthis was abnormal operation and that the end gun 70 may needmaintenance. In aspects, the logic for determining abnormal operationmay be based on a sliding window over seconds, minutes, hours, days,and/or years. In aspects, there is a traveling end gun 70 without spansknown as water winches. The disclosed technology also applies to waterwinches and lateral move irrigation systems. In aspects, a moveable endgun 70 may be disposed on the water winch. In some examples, a waterwinch moves on tires, in other examples, the end gun 70 movesrotationally by the drive arm, or a gear energized by water flow. In anexample, a water winch may be moved by another vehicle such as a tractoror a truck. In another example, the water winch may be pulled by aflexible water pipe pulls it along a path via a reel. In yet anotherexample, the end gun 70 may be directly mounted on a truck to keep dustdown in a mine, and/or to apply wastewater to a field. In anotherexample, the end gun 70 may not be mounted on the pivot, but rathermounted on a boom, and/or a last regular drive unit in the pivot styleirrigation system.

Monitoring output parameters such as end gun 70 timing, flow, an/orpressure can also help infer air compressor health. In aspects, abnormaloperation may further be determined by the water pressure and/or volumefrom the end gun 70.

For example, if a user (e.g., a farmer) was applying too much pressureto the end gun 70, and the water and fertilizer may get thrown over thecrop, leading to dry rings. The pressure sensor may sense that the endgun pressure was dropping to about 40 psi from a normal 71 psi. The endgun prediction model 502 may predict that the system is operatingabnormally based on the pressure measurement over time. The pressure mayhave been initially high, and then drop about 10 psi over the next hour.The farmer may have been operating at too high of a pressure because thebooster pump was dropping out and restarting frequently. The pumprestarting is very detrimental to the health of the irrigation system106, as the pump may wear out the electrical components well ahead oftheir rated life.

Electrical Instrumentation:

The system may also monitor contactors, commutator rings, motorwindings, electrical connections, and/or wiring failures. Monitoringelectrical transients or power metrics such as THD, Power Factor,current balance can help infer electrical system health.

Monitoring temperatures of the components listed above can also helpinfer electrical system health.

With reference to FIGS. 11A, 11B, and 12A-12C the movable end gun 70supports an electronics enclosure 1110 that supports at least one sensor1120 including an accelerometer, gyroscope, a microphone, a pressuresensor, flow sensor, and/or magnetometer, a power source or battery1130, a circuit 1140 (e.g., a controller), and/or a solar panel 1150that can be electrically coupled to one another. In aspects, the sensor1120 may be mounted overhead, underneath, and/or on the side of the endgun 70. In aspects, the sensor 1120 may include a water quality sensorthat measures, for example, iron, calcium, salts, and/or organicmaterial.

In aspects, the magnetometer may determine the heading and/or typicaltravel for an end gun 70 (see FIGS. 15 and 16). For example, typicaltravel for an end gun 70 may range from about 100 to about 150 degreesin rotation. If the drive arm return spring 1210 changes because of apoor setting, or due to a tree branch pulling it, heading accuracy maybe at least about 10 degrees. In other cases, end guns 70 never changedirection, or may travel outside of 100 to 150 degrees.

The movable end gun 70 can further support an encoder assembly 1160having an encoder 1162 and an encoder disc 1164 that is coupled toelectronics enclosure 1110. A pressure sensor 1170 is also coupled toelectronics enclosure 1110 to measure fluid flow pressure through endgun 70 (FIG. 15). Pressure may indicate the volume of water dispensed.Further, a reed switch 1180 or other magnetic switch can be coupled tomovable end gun 70 and disposed in proximity to a magnet 1190 supportedon the pivot 20 (FIG. 1). As can be appreciated, any the disclosedelectronics components can electrically couple to circuit 140 via wiredor wireless connection (see FIGS. 13A and 13B). Notably, one or more ofthe accelerometer, gyroscope, magnetometer, encoder assembly, and/or anyother suitable sensor(s) is configured to generate an electrical signalindicative of movement and/or positioning (e.g., acceleration, speed,distance, location, etc.) of the movable end gun 70 relative to thepivot 20 over time (seconds, minutes, hours, days, years, etc.). Thecontroller 200 is configured to receive the electrical signal anddetermine whether the movable end gun requires maintenance based on theelectrical signal. The controller 200 can send a signal and/or alertindicating the health of the end gun and/or whether maintenance isrequired thereon based on predetermined data or thresholds which may bepart of a database, program and/or stored in memory (e.g., supported onthe circuit, in the cloud, on a network, server, etc.).

When there is a mechanical problem with the end gun, the angular ratemay decrease. Furthermore, the ratio of time forward to time reverse maybecome less balanced and time spent going forward will become muchlonger than the return speed.

FIGS. 12A and 12B are example signals generated during one pass left toright of the end gun with the predictive maintenance system of FIG. 11A.In aspects, the end gun prediction model 502 may use ratios of factorssuch as total pass period (Tc), forward angular rate (T1), reverseangular rate (T2), number of forward turns (n), number of reverse turns(m), forward angular rate, and/or reverse angular rate, to indicatediminished health of the end gun. For example, an end gun in perfecthealth may have a ratio of forward angular rate to reverse angular rateof 1. Whereas this ratio may start to deviate from 1 as end gun healthdiminishes. In another example, a slope of the gyro signal over timeduring forward movement or reverse movement may be proportional toangular acceleration. This slope may be used by the end gun predictionmodel 502 to predict abnormal operation of the end gun. In aspects, whenthere is a mechanical problem, the angular rate may decrease.Furthermore, the ratio of time forward to time reverse may become lessbalanced and time spent going forward will become much longer than thereturn speed.

With reference to FIGS. 13A, 13B, and 14, the disclosed predictivemaintenance systems, which may be in the form of a smart end gun for endgun predictive maintenance, may operate using any suitable number ortype of analytics and/or logic approaches such as control charting,machine learning (“ML”) anomaly detection, parameter limit alarms, etc.For example, the predictive maintenance system may use a signal thatfails to meet a given threshold related to free movement of the end gun70 such as peak rotational speed during drive arm impact, or time ittakes to complete one Left-Right-Left Cycle, to predict abnormalbehavior.

In aspects, geolocation reporting may be used as an input to the MLmodel 300. For example, a GPS, may be used to determine a wet zoneversus a dry zone, and train as a “digital twin” as the irrigationsystem moves about the field. The altitude of the end gun 70 (relativeto the pivot) is also useful in predicting expected pressure. Pressuresignal analysis may be used as an input to the ML model 300.

FIG. 15 shows a flow chart for testing systems of the predictivemaintenance systems of this disclosure.

In aspects, the disclosed predictive maintenance systems can be aseparate system that can be selectively attached or retrofit to an endgun 70, or in some aspects, the predictive maintenance system can bebuilt directly into an end gun 70.

FIG. 17 shows a logic diagram for the disclosed technology. Thepredictive maintenance system may look at various movement acceptancecriteria such as forward/reverse angular rate, ratio of forward tobackward movement, angular range, time to trip detection lever,acceleration in x/y/z/forward/reverse directions, and/or heading changeforward and reverse. These movements are proportional to water pressure.In aspects, the slope of the accelerometer and/or gyro signal over time,and/or the waveforms from the gyro and/or accelerometer over time mayalso be used to determine abnormal operation of the end gun.

Moreover, the disclosed structure can include any suitable mechanical,electrical, and/or chemical components for operating the disclosed pivotpredictive maintenance system or components thereof. For instance, suchelectrical components can include, for example, any suitable electricaland/or electromechanical, and/or electrochemical circuitry, which mayinclude or be coupled to one or more printed circuit boards. As usedherein, the term “controller” includes “processor,” “digital processingdevice” and like terms, and are used to indicate a microprocessor orcentral processing unit (CPU). The CPU is the electronic circuitrywithin a computer that carries out the instructions of a computerprogram by performing the basic arithmetic, logical, control andinput/output (I/O) operations specified by the instructions, and by wayof non-limiting examples, include server computers. In some aspects, thecontroller includes an operating system configured to perform executableinstructions. Those of skill in the art will recognize that suitableserver operating systems include, by way of non-limiting examples,FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell® NetWare®. In some aspects, theoperating system is provided by cloud computing.

In some aspects, the term “controller” may be used to indicate a devicethat controls the transfer of data from a computer or computing deviceto a peripheral or separate device and vice versa, and/or a mechanicaland/or electromechanical device (e.g., a lever, knob, etc.) thatmechanically operates and/or actuates a peripheral or separate device.

In aspects, the controller includes a storage and/or memory device. Thestorage and/or memory device is one or more physical apparatus used tostore data or programs on a temporary or permanent basis. In someaspects, the controller includes volatile memory and requires power tomaintain stored information. In various aspects, the controller includesnon-volatile memory and retains stored information when it is notpowered. In some aspects, the non-volatile memory includes flash memory.In certain aspects, the non-volatile memory includes dynamicrandom-access memory (DRAM). In some aspects, the non-volatile memoryincludes ferroelectric random-access memory (FRAM). In various aspects,the non-volatile memory includes phase-change random access memory(PRAM). In certain aspects, the controller is a storage deviceincluding, by way of non-limiting examples, CD-ROMs, DVDs, flash memorydevices, magnetic disk drives, magnetic tapes drives, optical diskdrives, and cloud computing-based storage. In various aspects, thestorage and/or memory device is a combination of devices such as thosedisclosed herein.

In some aspects, the controller includes a display to send visualinformation to a user. In various aspects, the display is a cathode raytube (CRT). In various aspects, the display is a liquid crystal display(LCD). In certain aspects, the display is a thin film transistor liquidcrystal display (TFT-LCD). In aspects, the display is an organic lightemitting diode (OLED) display. In certain aspects, on OLED display is apassive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. Inaspects, the display is a plasma display. In certain aspects, thedisplay is a video projector. In various aspects, the display isinteractive (e.g., having a touch screen or a sensor such as a camera, a3D sensor, a LiDAR, a radar, etc.) that can detect userinteractions/gestures/responses and the like. In some aspects, thedisplay is a combination of devices such as those disclosed herein.

The controller may include or be coupled to a server and/or a network.As used herein, the term “server” includes “computer server,” “centralserver,” “main server,” and like terms to indicate a computer or deviceon a network that manages the system, components thereof, and/orresources thereof. As used herein, the term “network” can include anynetwork technology including, for instance, a cellular data network, awired network, a fiber optic network, a satellite network, and/or anIEEE 802.11a/b/g/n/ac wireless network, among others.

In various aspects, the controller can be coupled to a mesh network. Asused herein, a “mesh network” is a network topology in which each noderelays data for the network. All mesh nodes cooperate in thedistribution of data in the network. It can be applied to both wired andwireless networks. Wireless mesh networks can be considered a type of“Wireless ad hoc” network. Thus, wireless mesh networks are closelyrelated to Mobile ad hoc networks (MANETs). Although MANETs are notrestricted to a specific mesh network topology, Wireless ad hoc networksor MANETs can take any form of network topology. Mesh networks can relaymessages using either a flooding technique or a routing technique. Withrouting, the message is propagated along a path by hopping from node tonode until it reaches its destination. To ensure that all its paths areavailable, the network must allow for continuous connections and mustreconfigure itself around broken paths, using self-healing algorithmssuch as Shortest Path Bridging. Self-healing allows a routing-basednetwork to operate when a node breaks down or when a connection becomesunreliable. As a result, the network is typically quite reliable, asthere is often more than one path between a source and a destination inthe network. This concept can also apply to wired networks and tosoftware interaction. A mesh network whose nodes are all connected toeach other is a fully connected network.

In some aspects, the controller may include one or more modules. As usedherein, the term “module” and like terms are used to indicate aself-contained hardware component of the central server, which in turnincludes software modules. In software, a module is a part of a program.Programs are composed of one or more independently developed modulesthat are not combined until the program is linked. A single module cancontain one or several routines, or sections of programs that perform aparticular task.

As used herein, the controller includes software modules for managingvarious aspects and functions of the disclosed system or componentsthereof.

The disclosed structure may also utilize one or more controllers toreceive various information and transform the received information togenerate an output. The controller may include any type of computingdevice, computational circuit, or any type of processor or processingcircuit capable of executing a series of instructions that are stored inmemory. The controller may include multiple processors and/or multicorecentral processing units (CPUs) and may include any type of processor,such as a microprocessor, digital signal processor, microcontroller,programmable logic device (PLD), field programmable gate array (FPGA),or the like. The controller may also include a memory to store dataand/or instructions that, when executed by the one or more processors,cause the one or more processors to perform one or more methods and/oralgorithms.

Any of the herein described methods, programs, algorithms or codes maybe converted to, or expressed in, a programming language or computerprogram. The terms “programming language” and “computer program,” asused herein, each include any language used to specify instructions to acomputer, and include (but is not limited to) the following languagesand their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++,Delphi, Fortran, Java, JavaScript, machine code, operating systemcommand languages, Pascal, Perl, PL1, scripting languages, Visual Basic,metalanguages which themselves specify programs, and all first, second,third, fourth, fifth, or further generation computer languages. Alsoincluded are database and other data schemas, and any othermeta-languages. No distinction is made between languages which areinterpreted, compiled, or use both compiled and interpreted approaches.No distinction is made between compiled and source versions of aprogram. Thus, reference to a program, where the programming languagecould exist in more than one state (such as source, compiled, object, orlinked) is a reference to any and all such states. Reference to aprogram may encompass the actual instructions and/or the intent of thoseinstructions.

The machine learning (“ML”) model may be the most efficient for complexfailures. However, basic logic can be used for simpler failure modes.Likely signals of abnormal operation may come from increases in energyrequired to move the irrigation system, changes in speed of the system,or changes in sequence of the towers moving, end gun turn frequency, orpower quality metrics such as phase balance, inrush current, powerfactor, THD. Since these vary with a complex inference space, ML canassist in predicting abnormal operation and simplify user and subjectmatter expert input by giving a simple labeling method.

In aspects, the abnormal operation may be predicted by generating, basedon the received first set of sensor signals, a data structure that isformatted to be processed through one or more layers of a machinelearning model. The data structure may have one or more fieldsstructuring data. The abnormal operation may further be predicted byprocessing data that includes the data structure, through each of theone or more layers of the machine learning model that has been trainedto predict a likelihood that a particular piece of equipment may requiremaintenance; and generating, by an output layer of the machine learningmodel, an output data structure. The output data structure may includeone or more fields structuring data indicating a likelihood that aparticular piece of equipment may require maintenance. The abnormaloperation requirement may further be predicted by processing the outputdata structure to determine whether data organized by the one or morefields of the output data structure satisfies a predetermined threshold,wherein the output data structure includes one or more fieldsstructuring data indicating a likelihood that a particular piece ofequipment may require maintenance; and generating the prediction basedon the output data of the machine learning model. The predictionincludes the abnormal operation. The training may include supervisedlearning.

The machine learning model may be trained based on observing where theend gun 70 turns on, the speed at which the end gun 70 completes aLeft-Right-Left Cycle, peak gyro speed, GPS coordinates which the endgun 70 turns on, pressure at a given GPS location, and use that as a“digital twin” to set a baseline operation to compare to when inservice. In aspects, pressure transient data when the end gun 70 turnson and off may be used as an input to the machine learning model fortraining. The pressure transient data may be used to identify valveoperation issues that can lead to the valve sticking open or closed. Inaspects, irrigated acres of a field may be automatically mapped toreplace or supplement the current practice of “flagging,” by which anirrigation team member drives around the field with a laser range finderand places flags to create a field map that may be used to plant andmanage the field. Looking at the pressure at the end of the pivot 20 andon the end gun 70, the machine learning model may be able toidentify/predict potential issues in water supplied, well, well motors,spans, VFDs, filters, booster pumps, and/or other components of thepivot. The pressure transient measurements may be sensed using arelatively high time resolution (<100 msec). In aspects, a user mayconfigure the on and/or off zones of the end gun 70. In aspects, endgunkinematic parameters, as well as pressure transients for end gun valvepressure and/or corner arm sequencing pressure may be used as an inputto the machine learning model. This reference observation may be used toenable location and/or well pressure based analytics to improve thesensitivity and accuracy of the system.

In aspects, the controller 200 may receive the generated electricalsignal, from a sensor 102 monitoring a valve 902 (FIG. 9). The valve 902is configured to provide water for irrigation. The sensor 102 may sense,for example, but is not limited to pressure transient data. The sensor102 may sense the pressure transient data over time, and/or generate aresultant waveform over time. The controller 200 may determine whetherthe valve 902, or one or more components thereof, requires maintenancebased on the electrical signal and determine when the valve 902 requiresmaintenance (e.g., valve operation issues that can lead to the valvesticking open or closed) based on the electrical signal. Thedetermination may be performed by the machine learning model and/or by aclassical algorithm. The controller 200 may provide an alert to the userof the determination that the valve 902 requires maintenance.

Although an irrigation system is used as an example, the disclosedsystems and methods may be used advantageously in other environments,such as, but not limited to dust management in a mine, and/or irrigationof turf on a stadium be covered

In one aspect of the present disclosure, the disclosed algorithms may betrained using supervised learning. Supervised learning is the machinelearning task of learning a function that maps an input to an outputbased on example input-output pairs. The ML model infers a function fromlabeled training data consisting of a set of training examples. Insupervised learning, each example is a pair including an input object(typically a vector) and a desired output value (also called thesupervisory signal). A supervised learning algorithm analyzes thetraining data and produces an inferred function, which can be used formapping new examples. In various embodiments, the algorithm ma correctlydetermine the class labels for unseen instances. This requires thelearning algorithm to generalize from the training data to unseensituations in a “reasonable” way.

In various embodiments, the neural network may be trained using trainingdata, which may include, for example, different soil conditions ordifferent component characteristics (e.g., current, voltage, pressures,duty, etc.). The algorithm may analyze this training data and produce aninferred function that may allow the algorithm to identify componentfailures or changes in health, based on the generalizations thealgorithm has developed from the training data. In various embodiments,training may include at least one of supervised training, unsupervisedtraining, and/or reinforcement learning.

In some aspects, a user can initiate a training session while watchingoperation to simplify setup on each unique end gun and pivot combinationsince pressures and flows may differ. When the end gun is deemed to beoperating normally, the user can open a training window which will thenbe used to calibrate or train the analytics for future anomalydetection. For instance, Linux®, which may run a Python® script, forexample, can be utilized to effectuate prediction. In aspects, analyticsmay also be performed in the sensor using platforms such as Tensor Flow®lite.

In various embodiments, the neural network may include, for example, athree-layer temporal convolutional network with residual connections,where each layer may include three parallel convolutions, where thenumber of kernels and dilations increase from bottom to top, and wherethe number of convolutional filters increases from bottom to top. It iscontemplated that a higher or lower number of layers may be used. It iscontemplated that a higher or lower number of kernels and dilations mayalso be used.

In aspects, the disclosed monitoring systems can be a separate systemthat can be selectively attached or retrofit to an end gun, or in someaspects, the monitoring system can be built directly into an end gun.

As seen in FIGS. 19-21, a condition-based monitoring (CBM) system in theform of an end gun testing system is also provided for testing end gunsto determine health of end guns.

The system generally includes a first cloud server (e.g., a HeartlandFarm cloud) which includes an interface for the system, a device cloud(e.g., a Particle cloud) configured for communication between connecteddevices and the system, and a firmware server, which is configured topush firmware updates to components of the system. System devices mayinclude a cellular enabled microcontroller (e.g., a Particle Boron) anda CBM module. The cellular enabled microcontroller includes a cellularreceiver/transmitter/, a wireless receiver/transmitter (e.g., Bluetoothand/or WIFI), power management functions, firmware update functions,watchdog functions, power management integrated circuits (PMIC), poweron-self test (POST) functions, a universal asynchronousreceiver/transmitter (UART), and a general purpose IO (GPIO). Thecellular enabled microcontroller communicates with the condition-basedmonitor module (CBM module) which is configured for processing signalsfrom sensors. The sensor signals can be sampled by the CBM module at arate of 1125 KHz, for example. When the CBM module determines one ormore operations are completed, the CBM module notifies the system viathe cellular enabled microcontroller.

Referring to FIG. 20, a high-level block diagram of the CBM module isshown. The CBM module performs functions including signal processing,sensor calibration, power management, end gun operational report, endgun health alerts, end gun characterization, watchdog, bootloader, and astate machine. The CBM module generally includes a microcontroller(e.g., an STM32 microcontroller), a regulator, one or more UARTs, analogand/or digital inputs and outputs, a programming header (e.g., SWDand/or JTAG), status LED (e.g., status LED blue, error LED red), flashmemory, an impact sensor, an inertial measurement unit (IMU). Theapplication firmware and a bootloader firmware run on themicrocontroller. The CBM module may be powered via an external powersupply and/or a battery. An IMU is an electronic device that measuresand reports a body's specific force, angular rate, and sometimes theorientation of the body, using a combination of accelerometers,gyroscopes, and/or magnetometers.

The signal processing functions include, for example, logic to: detectwhen the end gun starts and/or stops, determine the number of degrees(both forward and reverse degrees) the end gun has traveled based on anonboard compass, detect the average peak forward and/or reverse angularrate based on an onboard gyro, determine the average forward and reversetime, determine a forward to reverse time ratio, determine the time totrip the reversing lever for the end gun (for example, to notify thesystem if the unit is getting hung up and is taking too long to reversedirection), drive arm forward and/or reverse frequency based on theaccelerometer and/or the gyro.

In aspects, a triangulated cellular location of the CBM module may beused to determine the end gun geolocation, speed, positioning, minimumand maximum observed location, and other relevant information. Cellularlocation data comes from a variety of sources, including cellphonetowers, Global Positioning System (GPS) signals, and Bluetooth beacons.For example, the cellular location of the CBM module may be determinedusing cell site location information since the cellular devices connecttheir users to telecommunications and internet networks through celltowers with known locations.

The CBM module may generate an operational report based on the processedsensor signals and can upload the report to the system via the cellularenabled microcontroller. The operational report may include, forexample, the total degrees traveled, the number of passes, average passdegrees traveled, and/or an average drive arm period.

Referring to FIG. 21, a diagram of the firmware is shown. The firmwaremay include, for example, the peripheral drivers (e.g., SPI, I2C, UART,and/or QUADSPI), the device drivers, and the application(s).

Referring to FIG. 22, a state machine for the CBM system is shown. Astate machine is a behavioral model including a finite number of states.Based on the current state and a given input, the state machine performsstate transitions. The state machine can include at least the followingstates: start (e.g., power on), boot load, initialization, applicationrecovery, idle, monitor, sensor calibration, end gun characterization,fault, hardware test, monitor, and/or sleep.

When the CBM module is initially powered up, the state machine starts inthe start state. Next, the CBM module enters the bootload state. In thebootload state, the bootloader firmware loads the application firmwareinto the working memory. The bootloader firmware may include the abilityto update the firmware, determine if the application is ok or not (e.g.,corrupt and/or wrong application), or load the application firmware. Thebootloader firmware may determine if the application firmware iscompatible with the hardware.

When in the bootload state, if the CBM module receives a predeterminedcommand (e.g., 0xAAFFAAFF”), the CBM module may enter a flash state(e.g., a firmware update mode). In this state, the CBM module canreceive firmware updates and then reset after updating the applicationfirmware.

If the application firmware is ok (e.g., not corrupt and/or wrongapplication), and a flash command is not received, then the applicationfirmware is loaded and the initialization state is entered. In theinitialization state, the CBM module sets up the sensors andcommunicates with the memory.

If initialization is ok, Idle state begins. In the Idle state, generallythe CBM module reads sensor data and processes the sensor data togenerate the operational report. While in the idle state, the CBM modulemay poll/interrogate the IMU to get the latest heading and/ortemperature from the device. In the idle state, if the gyro is activefor more than about 10 degrees per second, for about one minute, forinstance, the CBM module enters the monitor state, where the varioussensors are monitored.

While in the monitor state, if the gyro (e.g., the z-axis gyro) is lessthan about 5 degrees/second for about a minute, for instance, the CBMmodule exits the monitor state and re-enters the idle state. Upontransition to the monitor state, the CBM module will issue the end gunstart event. Upon exit of the monitor state, the CBM module will issuethe end gun stop event. Upon exit of the monitor state, the CBM modulemay send the operational report to the first cloud server. In themonitor state, the CBM module may determine, based on the sensors, thatone or more of the end guns have failed and may report the failure tothe first cloud server.

The CBM module may include a command line interface (CLI), which enablesa user computing device (e.g., a mobile device, a tablet, a desktopcomputer, and/or a laptop) connected to the CBM module via the UART(e.g., by RS485 and/or Bluetooth) to send commands to the CBM module toenter various states from the idle state. For example, a CLI command“CAL” causes the CBM module to enter the sensor calibration state. In asensor calibration state, the system may allow for the calibration ofthe sensors and sensor data. Additionally, if a TBS sensor fault isdetected during the monitor state, the CBM module may enter aself-recovery pass and re-enter the idle state. Faults may be based on atotal number of degrees traveled by the end gun, a number of passes, anaverage pass degree traveled, an average drive arm period, an averagepeak gyro output per drive arm impact, an average peak accelerometermagnitude per drive arm impact, an average forward and reverse timeratio, a total time in end gun on state, a number of faults reportedduring operation, a triangulated cellular location, a minimum heading,and/or a maximum heading. For example, a CLI command “HWtest” causes theCBM module to enter the hardware test state. In the hardware test state,the CBM module tests the attached hardware. For example, a CLI command“press enter twice” causes the CBM module to enter the monitor state.For example, a CLI command “CHAR” causes the CBM module to enter the endgun characterization state. In the end gun characterization state, theCBM module enables characterizing and scoring of the characterization ofthe attached hardware. For example, a CLI command “SLP” causes the CBMmodule to enter the sleep state.

FIG. 23 is a diagram of end gun zones (e.g., four quadrants) utilizedfor determining irrigation failure location. In aspects, one or more endguns may be located in the four quadrants of a field that also uses acenter pivot irrigation system. Even though four quadrants are used asan example, any number of end gun zones are contemplated to be withinthe scope of this disclosure. The end gun may be used to irrigate thecorners (e.g., quadrants) of a field that are missed by the center pivotirrigation system. Center pivot irrigation systems often take as long astwo full days to make a full circle around the field. If there is anequipment failure and the user does not notice that a corner is notirrigated on a hot summer day, for example, the crops in that corner maydie. Accordingly, the methods and systems for real-time missed cornerdetection of the disclosure help to avoid those situations by detectinga corner (e.g., quadrant) that was not irrigated and reporting this tothe user. In addition to not being irrigated, sometimes the end gun 70sticks “on” when it should be “off,” which can lead to hazards for homesand motorists in the area. In aspects, each quadrant may include two endguns equipped with a CBM module, for example. The CBM module may includea compass (e.g., a magnetometer) configured to determine a minimum and amaximum observed heading of the two end guns (e.g., a first end gun anda second end gun). For example, if the first end gun has a minimumobserved heading of southwest and the second end gun has a maximumobserved heading of northeast, then the controller 200 may determinethat the first and the second end gun are located in quadrant 4. Thefirst end gun for a quadrant may have a relationship to the first endgun for the next or previous quadrant, where they are offset inorientation by about 90 degrees. For example, the first end gun ofquadrant 4 may have a minimum observable position of southwest and thefirst end gun of quadrant 1 may have a minimum observable position ofnorthwest. In aspects, the CBM module may include a GPS, and/or usecellular location triangulation to set up a geofence to determine whichquadrant an end gun is located in. For example, based on the GPScoordinates, the controller 200 may determine that an end gun is inquadrant 2. The method may monitor for trigger “on” events to determineif an end gun has been triggered to irrigate the field. The controller200 may determine, based on the quadrant and whether a trigger on eventwas detected, that the determined quadrant was not irrigated. Inaspects, the user may receive a report or an indication (e.g., a textmessage, email, etc.) that the determined quadrant was not irrigated.For example, the controller 200 may detect a trigger on event for an endgun. The controller 200 also may detect that that specific end gun waslocated in quadrant 1. The controller 200 would determine that quadrant1 was irrigated. Next, the controller 200 may detect for the next endgun that there was no trigger on event, and based on the end gun's GPSlocation it was in quadrant 2. The controller 200 would determine thatquadrant 2 may not have been irrigated and would generate a report toalert the user so that the user can check out that end gun for possibleequipment failure. In the end gun zones, for example, the end gun 70 maybe turned “on” in a random location around the 360 degrees, not operatedin another corner, and/or in some cases, turned “on” all the time.

It is contemplated that the moveable end gun may be operatively coupledwith the pivot, e.g., as part of the system, but separate from thepivot. For example, in some less capitalized farms, a pivot may not havean end gun, but rather the end gun may be placed in a fixed location inthe corner of the field. The end gun may include stationary gun stylesprinklers that are set into place on a tripod or quadpod.

Referring to FIG. 24, a graph of example gyro signal output for themonitoring system of FIG. 1, is shown. For example, if an end gunrequires maintenance, data from the gyro may provide indications such asthe forward/reverse time ratio being higher than for an end gun thatdoes not require maintenance.

Referring to FIG. 25 a graph of example compass signal output for themonitoring system of FIG. 1, is shown. For example, if the end gun has ahard time flipping a reversing mechanism, there may be a slope change inthe output data from the compass (i.e., the magnetometer).

Referring to FIG. 26 a graph of example accelerometer signal output forthe monitoring system of FIG. 1, is shown. For example, if an end gunrequires maintenance, data from the accelerometer may provideindications such as the forward reverse time ratio being higher than foran end gun that does not require maintenance.

As can be appreciated, securement of any of the components of thedisclosed apparatus can be effectuated using known securement techniquessuch welding, crimping, gluing, fastening, etc.

Persons skilled in the art will understand that the structures andmethods specifically described herein and illustrated in theaccompanying figures are non-limiting exemplary aspects, and that thedescription, disclosure, and figures should be construed merely asexemplary of particular aspects. It is to be understood, therefore, thatthis disclosure is not limited to the precise aspects described, andthat various other changes and modifications may be effectuated by oneskilled in the art without departing from the scope or spirit of thedisclosure. Additionally, it is envisioned that the elements andfeatures illustrated or described in connection with one exemplaryaspect may be combined with the elements and features of another withoutdeparting from the scope of this disclosure, and that such modificationsand variations are also intended to be included within the scope of thisdisclosure. Indeed, any combination of any of the disclosed elements andfeatures is within the scope of this disclosure. Accordingly, thesubject matter of this disclosure is not to be limited by what has beenparticularly shown and described.

What is claimed is:
 1. A monitoring system for a pivot irrigationsystem, a movable end gun operatively associated with a portion of thepivot irrigation system, the monitoring system comprising: a sensorconfigured to couple to the movable end gun, and configured to generatean electrical signal indicative of movement and/or positioning of themovable end gun relative to the portion of the pivot irrigation systemover time; a processor; and a memory, including instructions storedthereon, which when executed by the processor cause the system to:receive the generated electrical signal; determine whether the movableend gun, or one or more components thereof, requires maintenance basedon the electrical signal; determine when the movable end gun is in an onand/or off trigger state based on the electrical signal; determine anangular rate of the movable end gun and time spent going forward and/orreverse based on the electrical signal; determine if the end gun pivotsmore than a predetermined number of degrees without an end gun ontrigger state; and provide an indication to a user that a location wasnot irrigated based on the determination.
 2. The monitoring system ofclaim 1 wherein the instructions, when executed, further cause thesystem to generate a report based on the determinations.
 3. Themonitoring system of claim 1, wherein the portion of the irrigationsystem includes at least one of a lateral drive, a water winch, or apivot, and wherein the movable end gun is movably mounted on the pivot.4. The monitoring system of claim 1, wherein the movable end gun is partof the same system but separate from the portion of the irrigationsystem.
 5. The monitoring system of claim 1, further comprising ananalytics engine configured to perform the determinations.
 6. Themonitoring system of claim 5, wherein the instructions, when executed bythe processor, further cause the monitoring system to receive data fromat least one of a weather station, a field soil moisture sensor, aterrain and soil map, a temperature sensor, or National Oceanic andAtmospheric Administration weather.
 7. The monitoring system of claim 5,wherein the analytics engine includes a machine learning model, andwherein the machine learning model is based on a deep learning network,a classical machine learning model, or combinations thereof.
 8. Themonitoring system of claim 1, wherein the sensor includes at least oneof an encoder, a pressure sensor, a flow meter, a magnetometer, agyroscope, an accelerometer, a camera, a gesture sensor, a microphone, alaser range finder, a reed switch, a magnetic switch, a GPS, or anoptical switch.
 9. A computer-implemented method for monitoring a pivotirrigation system including four end gun zones, each end gun zone, ofthe four end gun zones, including a movable end gun operativelyassociated with a portion of the pivot irrigation system, the methodcomprising: receiving an electrical signal generated by a sensorconfigured to couple to the movable end gun, wherein the electricalsignal is indicative of movement and/or positioning of the movable endgun relative to the portion of the pivot irrigation system over time;determining whether the movable end gun, or one or more componentsthereof, requires maintenance based on the electrical signal;determining when the movable end gun is in an on and/or off triggerstate based on the electrical signal; determining an angular rate of themovable end gun and time spent going forward and/or reverse based on theelectrical signal; determining if the end gun pivots more than apredetermined number of degrees without an end gun on trigger state; andproviding an indication to a user that a location was not irrigatedbased on the determination.
 10. The computer-implemented method of claim9 further comprising generating a report based on the determinations.11. The computer-implemented method of claim 9, wherein the portion ofthe irrigation system includes at least one of a lateral drive, a waterwinch, or a pivot, and wherein the movable end gun is movably mounted onthe pivot.
 12. The computer-implemented method of claim 9, wherein themovable end gun is part of the same system but separate from the portionof the irrigation system.
 13. The computer-implemented method of claim9, further comprising performing the determinations by an analyticsengine.
 14. The computer-implemented method of claim 13, furthercomprising receiving data from at least one of a weather station, afield soil moisture sensor, a terrain and soil map, a temperaturesensor, or National Oceanic and Atmospheric Administration weather. 15.The computer-implemented method of claim 13, wherein the analyticsengine includes a machine learning model, and wherein the machinelearning model is based on a deep learning network, a classical machinelearning model, or combinations thereof.
 16. A non-transitorycomputer-readable medium stores instructions that, when executed by aprocessor, cause the processor to perform a method for monitoring apivot irrigation system including a plurality of end gun zones, each endgun zone, of the plurality of end gun zones, including a movable end gunoperatively associated with a portion of the pivot irrigation system,the method comprising: receiving an electrical signal generated by asensor configured to couple to the movable end gun, wherein theelectrical signal is indicative of movement and/or positioning of themovable end gun relative to the portion of the pivot irrigation systemover time; determining whether the movable end gun, or one or morecomponents thereof, requires maintenance based on the electrical signal;determining when the movable end gun is in an on and/or off triggerstate based on the electrical signal; determining an angular rate of themovable end gun and time spent going forward and/or reverse based on theelectrical signal; determining if the end gun pivots more than apredetermined number of degrees without an end gun on trigger state; andproviding an indication to a user that a location was not irrigatedbased on the determination.